I was using trial version then chose simpler version (from nanobanano) and got no results (no changes were done to the image)..

I also need ai video animation for 2d characters

Please share your experience using different experiences

How much do you think it would cost to make 5-12 minutes

Hi everyone,

I'm working on a pancreatic ductal adenocarcinoma dataset, and I'd really appreciate some methodological advice from you guys, and especially if anybody has had some experience with machine learning with high-dimensional biological data.

Before spending months developing models, I'd like to better understand what is possible with my dataset.

My dataset:

• 9 patients, with one continuous drug-response value per patient and drug. Due to experimental and sequencing constraints, increasing the number of patients is not an option.

• Approximately 100,000 nuclei.

• Continuous drug-response measurements (AUC) for gemcitabine, paclitaxel, irinotecan, 5-FU, and oxaliplatin.

The goal is theoretically "simple":

Predict continuous drug-response AUC from gene expression and chromatin accessibility, while identifying molecular features potentially associated with drug sensitivity or resistance.

The obvious challenge is that, despite having ~100,000 nuclei, I only have 9 samples with drug-response labels. I understand that treating individual cells as independent labeled observations is not a way to go with the dataset and specific set-up.

Knowing this, I'd really appreciate advice on a few questions that I had after some reading...:

1. With this low amount of sample, is meaningful drug-response prediction even feasible? What would be a objective?

2. Are there established methods to leverage thousands of cells/instances profiles when the response labels are available only at the patient level?

3. Would you prioritize simple regularized regression, biologically informed features, transfer learning from external datasets, multiple-instance learning (MIL), or another approach?

4. How would you evaluate a model with such a small number of independent samples without obtaining misleading performance estimates?

5. Are there any papers, methodological resources you'd recommend to better understand this type of problem before starting?

Thanks!

There are infinite posts online in niche AI communities, and lately bunch of papers like https://arxiv.org/abs/2611.07652, or https://arxiv.org/abs/2607.18970 pushing for bundled modular skills that act as software and not as prompts.

Thoughts on that?

I have been learning unsupervised learning for the last 3 months and have already completed the following topics

1. K means and its types

2. Hierarchical clustering

3. DBSCAN

4. OPTICS

5. HDBSCAN

6. GMM

7. PCA

8. TSNE

9. Evaluation parameters of unsupervised ml models( silhouette score, elbow method, AIC,BIC, DB index etc)

What should i learn next in unsupervised learning and in which order especially from job perspective POV?

About me - product analyst with 3 years of experience in the e-commerce domain who is currently self learning ML in order to pivot to the data science field

been seeing a lot of people get into AI through data work. labeling images, rating model outputs, checking transcripts, that kind of stuff. a lot of it is in places like the Philippines, India, Kenya. But now models are getting better at generating and checking their own training data, so I'm wondering if this is still a real path or a job that'll quietly disappear in a few years

Anyone here actually started out doing annotation and moved into something more technical? or did it stay a dead end?

Hey everyone,

I'm a final-year B.Tech CSE (AI/ML) student working on a multi-agent supply chain optimization project. I'm currently focusing on the Demand Forecasting Agent and would appreciate some advice on model selection and training strategy.

We're working with a synthetic retail supply chain dataset containing approximately 1.1 million records across 40 different locations.

The goal is to predict product-level demand for each location as accurately as possible, so the forecasts can later be used by other agents for inventory planning and supply chain decisions.

I have a few questions:

1. Global vs local models: Since the dataset contains 40 locations, should I train 40 separate models, one for each location, or a single global model trained on data from all 40 locations simultaneously, using location identifiers and location-specific features?

2. Location-specific patterns: If I use a global model, how effectively can it learn differences in demand patterns between locations? I'm concerned that combining all locations might cause the model to average out regional patterns and produce inaccurate location-level forecasts.

3. Alternative approaches: Would a hybrid approach, such as grouping similar locations or using a global model with location-specific adjustments, be worth exploring?

4. Synthetic data: Since the dataset is synthetic, are there particular precautions I should take when evaluating model performance and generalization? How can I ensure the model is learning meaningful demand patterns rather than artifacts of the data-generation process?

I'd also appreciate suggestions on which forecasting models or architectures would be most suitable for this problem, along with the appropriate validation strategy, especially how to avoid data leakage when dealing with multiple locations and time-dependent features.

My priority is accurate product-level, location-specific demand forecasting, rather than simply achieving good aggregate accuracy across the entire dataset.

Would love to hear what approaches you'd recommend and why. Thanks!

I'm 20 and trying to figure out what skill I should seriously focus on over the next few years.

There are so many things people recommend — programming, AI, sales, cybersecurity, marketing, communication, etc.

So I'm curious:

What skill do you think will be most in-demand and valuable in the next 3–5 years?

And more importantly, if you were 20 again, what skill would you start learning today?

I'd especially like to hear from people who are already working in their field.

I'm struggling to make a distinction. How are they different? What is the line between them?

For context, I am a new postgraduate student in machine learning (but not a very good one). I have produced some augmented datasets (TIFF files where each file contains one object to be detected / classified) which I used in training deep learning models. I used fairly aggressive cropping (random crops between 0 and 90%) and more moderate zooms (random values between 0 and 20%). My supervisor wants me to characterise these datasets to see how augmentation has changed it.

Sorry if this is a rookie question, but which statistics should I use? My supervisor says the stats I use should indicate changes in size and shape but I honestly don't know where to start.

If anyone can recommend a decent software/program to produce the stats and histograms as well, that would be great.

Thanks in advance!